Fault Diagnosis and Monitoring in Proton Exchange Membrane Fuel Cell Systems

Proton exchange membrane fuel cells (PEMFCs) offer a promising route to clean energy conversion, yet their widespread adoption hinges on robust strategies for fault detection and system monitoring. Faults may arise from water management imbalances, membrane degradation, gas starvation, catalyst poisoning or sensor malfunctions. If left unaddressed, these events accelerate performance decline, reduce efficiency and shorten operational lifetime. Traditional model-based methods employ equivalent circuit or first-principles models to interpret voltage and impedance signatures, but they often struggle with high computational burden and sensitivity to parameter uncertainties. In response, data-driven approaches—including machine learning classifiers, statistical inference and hybrid frameworks—have gained prominence for their capacity to handle complex, nonlinear behaviour using real-time sensor arrays. Key monitoring modalities encompass electrochemical impedance spectroscopy for in situ characterisation, sensor fusion techniques to consolidate pressure, temperature and humidity readings, and digital-twin platforms that integrate physics-based models with operational data. Together, these tools enable early warning of anomalies, support condition-based maintenance and enhance the reliability of PEMFC stacks across automotive, stationary and portable applications. International efforts continue to refine algorithms for rapid, on-board diagnostics and to develop low-cost, scalable sensor networks that preserve accuracy while minimising system intrusion.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Fault Diagnosis and Monitoring in Proton Exchange Membrane Fuel Cell Systems publication trend

The graph below shows the total number of articles in fault diagnosis and monitoring in proton exchange membrane fuel cell systems across all publications each year (not limited to Nature Index journals).

Technical terms

Proton Exchange Membrane Fuel Cell (PEMFC): An electrochemical device that converts hydrogen and oxygen into electricity and water using a polymer electrolyte membrane.

Fault diagnosis: The process of detecting, isolating and identifying abnormal conditions or failures within a system to prevent performance degradation.

Support Vector Machine (SVM): A supervised learning algorithm that classifies data by finding an optimal hyperplane separating different fault classes.

Genetic Algorithm (GA): An optimisation heuristic inspired by natural selection, used to adjust model parameters for improved diagnostic accuracy.

Sensor reliability: A measure of the consistency and trustworthiness of sensor outputs, critical for ensuring valid condition-monitoring inputs.

References

1. An Equivalent Electrical Circuit Model of Proton Exchange Membrane Fuel Cells Based on Mathematical Modelling. Energies (2012).
2. Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests. Energies (2020).
3. A New, Scalable and Low Cost Multi-Channel Monitoring System for Polymer Electrolyte Fuel Cells. Sensors (2016).
4. Investigation of PEMFC fault diagnosis with consideration of sensor reliability. International Journal of Hydrogen Energy (2018).
5. Research on Fuel Cell Fault Diagnosis Based on Genetic Algorithm Optimization of Support Vector Machine †. Energies (2022).
6. Hydrogen Leakage Diagnosis for Proton Exchange Membrane Fuel Cell Systems: Methods and Suggestions on Its Application in Fuel Cell Vehicles. IEEE Access (2020).